var accumulator: (Long, Long) = (0L, 0L) val a2: DataStream[Double] = f2.map(x => { val jsonObject = JsonMethods.parse(x) val JDouble(sbljl) = (jsonObject \ "order_status").extract[JDouble] if (sbljl.equals(JDouble(1001))) { accumulator = (accumulator._1 + 1L, accumulator._2 + 1L) } else { accumulator = (accumulator._1, accumulator._2 + 1L) } accumulator._1 / accumulator._2.toDouble }).keyBy(0) .windowAll(TumblingProcessingTimeWindows.of(Time.minutes(1))) a2.map(x => { val cancelRate = x // 计算取消订单占比 val roundedCancelRate = BigDecimal(cancelRate).setScale(1, BigDecimal.RoundingMode.HALF_UP).toDouble // 四舍五入保留一位小数 ("someKey", roundedCancelRate) // 使用一个虚拟的key }).addSink(new RedisSink[(String, Double)](flinkJedisPoolConfig, new TotalPriceRedisMapper2)).windowAll(TumblingProcessingTimeWindows.of(Time.minutes(1))类型不匹配

python入门:argparse浅析 nargs=+作用

parser.add_argument('integers', metavar='N', type=int, nargs='+', help='an integer for the accumulator') 这里，integers是一个位置参数，用户在命令行中输入的任何整数都会被收集到integers列表中。...

val accumulator: (Long, Long) = (0L, 0L) val a2: DataStream[Double] = f2.map(x => { val jsonObject = JsonMethods.parse(x) val JDouble(sbljl) = (jsonObject \ "order_status").extract[JDouble] if (sbljl.equals(JDouble(1001))) { (accumulator._1 + 1L, accumulator._2 + 1L) } else { (accumulator._1, accumulator._2 + 1L) } }).keyBy(0) .windowAll(TumblingProcessingTimeWindows.of(Time.minutes(1))) .aggregate(new CustomAggregateFunction()) a2.map(x => { val cancelRate = x / accumulator._2.toDouble // 计算取消订单占比 val roundedCancelRate = BigDecimal(cancelRate).setScale(1, BigDecimal.RoundingMode.HALF_UP).toDouble // 四舍五入保留一位小数 (key, roundedCancelRate) }).addSink(new RedisSink[(String, Double)](flinkJedisPoolConfig, new TotalPriceRedisMapper2)) env.execute() class TotalPriceRedisMapper2 extends RedisMapper[(String, Double)] { override def getCommandDescription: RedisCommandDescription = { new RedisCommandDescription(RedisCommand.SET, "cancelrate") } override def getKeyFromData(data: (String, Double)): String = data._1 override def getValueFromData(data: (String, Double)): String = data._2.toString }无法解析符号 CustomAggregateFunction解决办法

根据您提供的代码，无法解析符号CustomAggregateFunction。这意味着您在代码中引用了一个未定义的CustomAggregateFunction类。解决此问题的步骤如下： 1. 确保您已经导入所需的CustomAggregateFunction类。...

class Accumulator: """在n个变量上累加""" def init(self, n): self.data = [0.0] * n def add(self, args): self.data = [a + float(b) for a, b in zip(self.data, args)] def reset(self): self.data = [0.0] len(self.data) def getitem(self, idx): return self.data[idx] evaluate_accuracy(net, test_iter)

这段代码中定义了一个累加器类 Accumulator，其在构造函数中初始化了一个长度为 n 的数组，然后在 add 方法中将传入的参数与数组中的元素相加，并将结果保存到数组中。reset 方法将数组中的所有元素都清零。最后，...

def evaluate_accuracy(net, data_iter): if isinstance(net, torch.nn.Module): net.eval() metric = Accumulator(2) for X, y in data_iter: metric.add(accuracy(net(X), y), y.numel()) return metric[0] / metric[1] 中metric = Accumulator(2)这一行是什么意思，其中Accumulator类如下： class Accumulator: """在n个变量上累加。""" def init(self, n): self.data = [0.0] * n def add(self, args): self.data = [a + float(b) for a, b in zip(self.data, args)] def reset(self): self.data = [0.0] len(self.data) def getitem(self, idx): return self.data[idx]

这行代码metric = Accumulator(2)的意思是创建了一个Accumulator类的实例，该实例有两个变量用于累加数据。在这个特定的例子中，metric用于累加模型预测的准确率和样本数量。 Accumulator类的定义如下： ...

def train(net, train_iter, val_iter, num_epochs, lr, wd, devices, lr_period, lr_decay): global val_acc, metric trainer = torch.optim.SGD(net.patameters(), lr=lr, momentum=0.9, weight_decay=wd) scheduler = torch.optim.lr_scheduler.StepLR(trainer, lr_period, lr_decay) num_batches, timer = len(train_iter), d2l.Timer() legend = ['train loss', 'train acc'] # 画出来的图的线条标签 if val_iter is not None: legend.append('valid acc') animator = d2l.Animator(xlabel='epoch', xlim=[1, num_epochs], legend=legend) net = nn.DataParallel(net, device_ids=0).to(device) for epoch in range(num_epochs): # 开始训练 net.train() metric = d2l.Accumulator(3) for i, (features, labels) in enumerate(train_iter): timer.start() l, acc = d2l.train_batch_ch13(net, features, labels, loss, trainer, devices) metric.add(l, acc, labels.shape[0]) timer.stop() if (i + 1) % (num_batches // 5) == 0 or i == num_batches - 1: animator.add(epoch + (i + 1) / num_batches, (metric[0] / metric[2], metric[1] / metric[2], None)) if val_iter is not None: val_acc = d2l.evaluate_accuracy_gpu(net, val_iter) animator.add(epoch + 1, (None, None, val_acc)) scheduler.step() measures = (f'train loss {metric[0] / metric[2]:.3f},'f'train acc {metric[1] / metric[2]:.3f}') if val_iter is not None: measures += f', val acc {val_acc :.3f}'检查并优化

metric = d2l.Accumulator(3) for i, (features, labels) in enumerate(train_iter): timer.start() l, acc = d2l.train_batch_ch13(net, features, labels, loss, trainer, devices) metric.add(l, acc, ...

def train(net, train_iter, val_iter, num_epochs, lr, wd, devices, lr_period, lr_decay): global val_acc, metric net.to(devices[0]) trainer = torch.optim.SGD(net.parameters(), lr=lr, momentum=0.9, weight_decay=wd) scheduler = torch.optim.lr_scheduler.StepLR(trainer, lr_period, lr_decay) num_batches, timer = len(train_iter), d2l.Timer() legend = ['train loss', 'train acc'] if val_iter is not None: legend.append('val acc') animator = Animator(xlabel='epoch', xlim=[1, num_epochs], legend=legend) net = nn.DataParallel(net, device_ids=devices).to(devices[0]) for epoch in range(num_epochs): net.train() metric = d2l.Accumulator(3) for i, (features, labels) in enumerate(train_iter): timer.start() l, acc = d2l.train_batch_ch13(net, features, labels, loss, trainer, devices) metric.add(l, acc, labels.shape[0]) timer.stop() if (i + 1) % (num_batches // 5) == 0 or i == num_batches - 1: animator.add(epoch + (i + 1) / num_batches, ( metric[0] / metric[2], metric[1] / metric[2], None)) if val_iter is not None: val_acc = d2l.evaluate_accuracy_gpu(net, val_iter) animator.add(epoch + 1, (None, None, val_acc)) scheduler.step() measures = (f'train loss {metric[0] / metric[2]:.3f},' f'train acc {metric[1] / metric[2]:.3f}') if val_iter is not None: measures += f',valid acc {val_acc:.3f}' print(measures + f'\n {metric[2] * num_epochs / timer.sum():.1f}' f'examples /sec on {str(devices)}') 代码在jupyter中可以画图，但是在pycharm中不显示图像

这是因为在PyCharm中默认情况下无法自动显示Matplotlib图形窗口，您需要在代码中添加以下两行代码： python import matplotlib.pyplot as plt plt.show() 这将强制显示Matplotlib图形窗口。...

def train(net, train_iter, val_iter, num_epochs, lr, wd, devices, lr_period, lr_decay): global val_acc, metric net.to(devices[0]) trainer = torch.optim.SGD(net.parameters(), lr=lr, momentum=0.9, weight_decay=wd) scheduler = torch.optim.lr_scheduler.StepLR(trainer, lr_period, lr_decay) num_batches, timer = len(train_iter), d2l.Timer() train_loss, train_accs, val_accs = [], [], [] for epoch in range(num_epochs): net.train() metric = d2l.Accumulator(3) for i, (features, labels) in enumerate(train_iter): trainer.zero_grad() features, labels = features.to(devices[0]), labels.to(devices[0]) l, acc = d2l.train_batch_ch13(net, features, labels, loss, trainer, devices) metric.add(l, acc, labels.shape[0]) train_loss.append(metric[0] / metric[2]) train_accs.append(metric[1] / metric[2]) if val_iter is not None: val_acc = d2l.evaluate_accuracy_gpu(net, val_iter) val_accs.append(val_acc) d2l.plot(range(1, epoch + 2), [train_loss, train_accs, val_accs], xlabel='epoch', legend=['train loss', 'train acc', 'val acc'], figsize=(8, 6)) scheduler.step() measures = (f'train loss {metric[0] / metric[2]:.3f},'f'train acc {metric[1] / metric[2]:.3f}') if val_iter is not None: measures += f', val acc {val_acc :.3f}' print(measures + f'\n {metric[2] * num_epochs / timer.sum() :.1f}' f'examples /sec on {str(devices)}')以上代码可以在pycharm中帮我实现画图功能吗？请优化代码

metric = d2l.Accumulator(3) for i, (features, labels) in enumerate(train_iter): trainer.zero_grad() features, labels = features.to(devices[0]), labels.to(devices[0]) l, acc = d2l.train_batch_ch13...

def train(net, train_iter, val_iter, num_epochs, lr, wd, devices, lr_period, lr_decay): global val_acc, metric net.to(devices[0]) trainer = torch.optim.SGD(net.parameters(), lr=lr, momentum=0.9, weight_decay=wd) scheduler = torch.optim.lr_scheduler.StepLR(trainer, lr_period, lr_decay) num_batches, timer = len(train_iter), d2l.Timer() train_loss, train_accs, val_accs = [], [], [] fig, ax = plt.subplots(figsize=(8, 6)) for epoch in range(num_epochs): net.train() metric = d2l.Accumulator(3) for i, (features, labels) in enumerate(train_iter): trainer.zero_grad() features, labels = features.to(devices[0]), labels.to(devices[0]) l, acc = d2l.train_batch_ch13(net, features, labels, loss, trainer, devices) metric.add(l, acc, labels.shape[0]) train_loss.append(metric[0] / metric[2]) train_accs.append(metric[1] / metric[2]) if val_iter is not None: val_acc = d2l.evaluate_accuracy_gpu(net, val_iter) val_accs.append(val_acc) ax.plot(range(1, epoch + 2), [train_loss, train_accs, val_accs]) ax.set_xlabel('epoch') ax.set_ylabel('value') ax.legend(['train loss', 'train acc', 'val acc']) plt.show() scheduler.step() measures = ( f'train loss {metric[0] / metric[2]:.3f},'f'train acc {metric[1] / metric[2]:.3f}') if val_iter is not None: measures += f', val acc {val_acc :.3f}' print(以上代码能否实现时事图像展示？请优化成不等epoch结束就时事显示图像的代码

if (i + 1) % 100 == 0: # 每训练 100 个 batch，实时显示图像 train_loss.append(metric[0] / metric[2]) train_accs.append(metric[1] / metric[2]) if val_iter is not None: val_acc = d2l.evaluate_...

def train(net, train_iter, val_iter, num_epochs, lr, wd, devices, lr_period, lr_decay): global val_acc, metric trainer = torch.optim.SGD(net.parameters(), lr=lr, momentum=0.9, weight_decay=wd) scheduler = torch.optim.lr_scheduler.StepLR(trainer, lr_period, lr_decay) num_batches, timer = len(train_iter), d2l.Timer() train_loss, train_accs, val_accs = [], [], [] for epoch in range(num_epochs): net.train() metric = d2l.Accumulator(3) for i, (features, labels) in enumerate(train_iter): trainer.zero_grad() features, labels = features.to(devices[0]), labels.to(devices[0]) l, acc = d2l.train_batch_ch13(net, features, labels, loss, trainer, devices) metric.add(l, acc, labels.shape[0]) train_loss.append(metric[0] / metric[2]) train_accs.append(metric[1] / metric[2]) if val_iter is not None: val_acc = d2l.evaluate_accuracy_gpu(net, val_iter) val_accs.append(val_acc) d2l.plot(range(1, epoch + 2), [train_loss, train_accs, val_accs], xlabel='epoch', legend=['train loss', 'train acc', 'val acc'], figsize=(8, 6)) scheduler.step() TypeError: 'torch.device' object is not subscriptable

features, labels = features.to(devices[0]), labels.to(devices[0]) 要解决这个问题，需要确保 devices 参数传递进来的是一个列表或元组类型的对象，而不是一个单独的 torch.device 类型的对象。可以在...

def train_epoch_ch3(net, train_iter, loss, updater): """训练模型一个迭代周期（定义见第3章）。""" if isinstance(net, torch.nn.Module): net.train() metric = Accumulator(3) for X, y in train_iter: y_hat = net(X) l = loss(y_hat, y) if isinstance(updater, torch.optim.Optimizer): updater.zero_grad() l.backward() updater.step() metric.add( float(l) * len(y), accuracy(y_hat, y), y.size().numel()) else: l.sum().backward() updater(X.shape[0]) metric.add(float(l.sum()), accuracy(y_hat, y), y.numel()) return metric[0] / metric[2], metric[1] / metric[2]

在这种情况下，首先计算损失值的和作为最终的损失值（l.sum().backward()），然后调用updater函数来更新参数（updater(X.shape[0])）。最后，将损失值、准确率和样本数量传入metric.add()进行累加。 ...

oncheck() { const me = this if (!me.selectedRows.length) { me.warn('请先选择记录！') return } this.confirm('确认添加所选择的记录？').then((pass) => { var newArr = [] me.selectedRows.map(function (value) { if (value.containerId) { value.containerId.map((item) => { newArr.push(item) }) } }) this.containerList = Array.from(new Set(newArr)) this.paramsIn = { where: { 't.entrepotNo': this.$bus.tmsEntrepotNo, 'd.containerId_in': '', }, } this.paramsIn.where['d.containerId_in'] = this.containerList.join(',') this.paramsIn.where=JSON.stringify(this.paramsIn.where) this.$httpPost(this.ajax.getList, this.paramsIn,false).then((res) => { if (res.flag.retCode != '0') { me.error(res.flag.retMsg) } else { let list = this.selectedRows.concat(res.rows) let keys = {} let listA = list.reduce((accumulator, currentValue) => { !keys[currentValue['billNo']] ? (keys[currentValue['billNo']] = true && accumulator.push(currentValue)) : null return accumulator }, []) this.$emit('fatherclose', listA) } }) }) },

8. 在返回结果的回调函数中，首先判断 res.flag.retCode 是否为 '0'，如果不是，则调用 me.error(res.flag.retMsg) 提示用户错误信息。 9. 如果 res.flag.retCode 是 '0'，则将 res.rows 数组与 ...

#@save def train_ch6(net, train_iter, test_iter, num_epochs, lr, device): """用GPU训练模型(在第六章定义)""" def init_weights(m): if type(m) == nn.Linear or type(m) == nn.Conv2d: nn.init.xavier_uniform_(m.weight) net.apply(init_weights) print('training on', device) net.to(device) optimizer = torch.optim.SGD(net.parameters(), lr=lr) loss = nn.CrossEntropyLoss() animator = d2l.Animator(xlabel='epoch', xlim=[1, num_epochs], legend=['train loss', 'train acc', 'test acc']) timer, num_batches = d2l.Timer(), len(train_iter) for epoch in range(num_epochs): # 训练损失之和，训练准确率之和，样本数 metric = d2l.Accumulator(3) net.train() for i, (X, y) in enumerate(train_iter): timer.start() optimizer.zero_grad() X, y = X.to(device), y.to(device) y_hat = net(X) l = loss(y_hat, y) l.backward() optimizer.step() with torch.no_grad(): metric.add(l * X.shape[0], d2l.accuracy(y_hat, y), X.shape[0]) timer.stop() train_l = metric[0] / metric[2] train_acc = metric[1] / metric[2] if (i + 1) % (num_batches // 5) == 0 or i == num_batches - 1: animator.add(epoch + (i + 1) / num_batches, (train_l, train_acc, None)) test_acc = evaluate_accuracy_gpu(net, test_iter) animator.add(epoch + 1, (None, None, test_acc)) print(f'loss {train_l:.3f}, train acc {train_acc:.3f}, ' f'test acc {test_acc:.3f}') print(f'{metric[2] * num_epochs / timer.sum():.1f} examples/sec ' f'on {str(device)}')

这段代码是用GPU训练模型的代码，可以使用SGD优化器和交叉熵损失函数对模型进行训练，并且使用Accumulator类来计算训练损失之和、训练准确率之和和样本数，以及使用d2l.Animator类绘制训练和测试的准确率和损失的...

def evaluate_accuracy_gpu(net, data_iter, device=None): #@save """使⽤GPU计算模型在数据集上的精度""" if isinstance(net, nn.Module): net.eval() # 设置为评估模式 if not device: device = next(iter(net.parameters())).device # 正确预测的数量，总预测的数量 metric = d2l.Accumulator(2) with torch.no_grad(): for X, y in data_iter: if isinstance(X, list): # BERT微调所需的（之后将介绍） X = [x.to(device) for x in X] else: X = X.to(device) y = y.to(device) metric.add(d2l.accuracy(net(X), y), y.numel()) return metric[0] / metric[1]

接着使用 torch.no_grad() 来关闭梯度计算，在循环中将数据 X 和标签 y 移动到指定的设备上，使用 net(X) 来进行预测并计算精度，并将结果添加到累加器中。最后返回正确预测的数量除以总预测的数量，即模型在数据集...

#@save def train_batch_ch13(net, X, y, loss, trainer, devices): """用多GPU进行小批量训练""" if isinstance(X, list): # 微调BERT中所需 X = [x.to(devices[0]) for x in X] else: X = X.to(devices[0]) y = y.to(devices[0]) net.train() trainer.zero_grad() pred = net(X) l = loss(pred, y) l.sum().backward() trainer.step() train_loss_sum = l.sum() train_acc_sum = d2l.accuracy(pred, y) return train_loss_sum, train_acc_sum #@save def train_ch13(net, train_iter, test_iter, loss, trainer, num_epochs, devices=d2l.try_all_gpus()): """用多GPU进行模型训练""" timer, num_batches = d2l.Timer(), len(train_iter) animator = d2l.Animator(xlabel='epoch', xlim=[1, num_epochs], ylim=[0, 1], legend=['train loss', 'train acc', 'test acc']) net = nn.DataParallel(net, device_ids=devices).to(devices[0]) for epoch in range(num_epochs): # 4个维度：储存训练损失，训练准确度，实例数，特点数 metric = d2l.Accumulator(4) for i, (features, labels) in enumerate(train_iter): timer.start() l, acc = train_batch_ch13( net, features, labels, loss, trainer, devices) metric.add(l, acc, labels.shape[0], labels.numel()) timer.stop() if (i + 1) % (num_batches // 5) == 0 or i == num_batches - 1: animator.add(epoch + (i + 1) / num_batches, (metric[0] / metric[2], metric[1] / metric[3], None)) test_acc = d2l.evaluate_accuracy_gpu(net, test_iter) animator.add(epoch + 1, (None, None, test_acc)) print(f'loss {metric[0] / metric[2]:.3f}, train acc ' f'{metric[1] / metric[3]:.3f}, test acc {test_acc:.3f}') print(f'{metric[2] * num_epochs / timer.sum():.1f} examples/sec on ' f'{str(devices)}')

这段代码是用多GPU进行小...最后，train_ch13函数使用Accumulator类来计算训练损失、训练准确率、实例数和特征数，并使用Animator类将这些指标可视化。同时，该函数还使用evaluate_accuracy_gpu函数计算测试准确率。

ds = MyDataset("C:\\Users\\12402\\Documents\\C4-Net\\FlowRecognition_model\\dataset") f2c_dict = read_fine("./dataset/fine.txt") c2b_dict = { "normal": "T", "unknown": "F", "dos": "F", "probe": "F" } metric = Accumulator(4) b1 = [0.9110, 0.1806, 0.1747, 0.6423, 0.7139, 0.7204, 0.7982, 0.2368, 0.6226, 0.3725, 0.6620, 0.9948] b2 = [0.4234, 0.7067, 0.3852, 0.6186, 0.8946, 0.3734, 0.5420, 0.4267, 0.5648, 0.0110, 0.3604, 0.6843] b3 = [0.8936, 0.9608, 0.7194, 0.4204, 0.4181, 0.7879, 0.3307, 0.0184, 0.8996, 0.7674, 0.5716, 0.8036] output = torch.from_numpy(np.array([b1, b2, b3])) print(output) label = torch.from_numpy(np.array([0, 4, 1])) accuracy(output, label, metric, f2c_dict, c2b_dict, ds.dict) print(metric[0], metric[1], metric[2], metric[3])

这段代码是关于一个流量识别模型的评估。其中，MyDataset是一个自定义的数据集类，read_fine函数读取了一个包含细分类标签的文件fine.txt。c2b_dict将粗分类标签映射到二元标签，metric是一个累加器，用来统计模型的...

export default { name: 'national_id', validator(value) { const valueArray = Array.from(value, (val) => Number(val)) const reducer1 = (accumulator, currentValue, index) => accumulator + currentValue * (10 - index) const array1 = valueArray.slice(0, 9) const firstMod11 = array1.reduce(reducer1, 0) % 11 const firstCheckDigit = firstMod11 < 2 ? 0 : 11 - firstMod11 const reducer2 = (accumulator, currentValue, index) => accumulator + currentValue * (11 - index) const array2 = valueArray.slice(0, 10) const secondMod11 = array2.reduce(reducer2, 0) % 11 const secondCheckDigit = secondMod11 < 2 ? 0 : 11 - secondMod11 const checkDigits = firstCheckDigit * 10 + secondCheckDigit const inputCheckDigits = Number(value.slice(9, 13)) return checkDigits === inputCheckDigits } } 解释这段代码

第 10 位数字的权重为 0，所以不需要进行乘法计算。然后，该方法将第 1 到第 10 位数字分别乘以不同的权重（分别是 11-1），然后将它们的乘积相加，对 11 取模，得到第二个校验位。最后，该方法将第一个和第二个...

如何理解代码：metric = d2l.Accumulator(2)

d2l.Accumulator(2)中的2表示累加器的长度，即它可以保存2个数值。这个累加器对象可以用于记录模型的指标，例如准确率、损失等。每次向累加器对象添加一个新的值时，它会将这个值与之前的值相加，最终得出累加器中...

基于net的超市管理系统源代码（完整前后端+sqlserver+说明文档+LW）.zip

功能说明：环境说明：开发软件：VS 2017 （版本2017以上即可，不能低于2017）数据库：SqlServer2008r2（数据库版本无限制，都可以导入）开发模式：mvc。。。

相关推荐

python入门:argparse浅析 nargs=+作用

如何理解代码：metric = d2l.Accumulator(2)

基于net的超市管理系统源代码（完整前后端+sqlserver+说明文档+LW）.zip

最新推荐

Windows平台下的Fastboot工具使用指南

管理建模和仿真的文件

DLMS规约深度剖析：从基础到电力通信标准的全面掌握

修改代码，使其正确运行

Python机器学习基础入门与项目实践

"互动学习：行动中的多样性与论文攻读经历"

【Shell脚本进阶】：wc命令行数统计的高级用法及解决方案

python编写一个程序，使得根据输入的起点和终点坐标值计算出坐标方位角

Achilles-2 原始压缩包内容解密

关系数据表示学习